Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms-Reference-Cited by-同舟云学术

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Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms

Published:2024-06 Issue: Volume:155 Page:107578
ISSN:0735-1933
Container-title:International Communications in Heat and Mass Transfer
language:en
Short-container-title:International Communications in Heat and Mass Transfer

Author:

Jin WeiHong,Basem Ali,Baghoolizadeh Mohammadreza,Kamoon Saeed S.,Al-Yasiri Mortatha,Salahshour Soheil,Hekmatifar Maboud

Publisher

Elsevier BV

Reference67 articles.

1. A comprehensive review on the natural, forced, and mixed convection of non-Newtonian fluids (nanofluids) inside different cavities;Yang;J. Therm. Anal. Calorim.,2020

2. Enhancing Thermal Conductivity of Fluids with Nanoparticles;Choi,1995

3. Thermal conductivity of nanofluids;Singh;Def. Sci. J.,2008

4. Review on Nano-fluids applications and heat transfer enhancement techniques in different enclosures;Mebarek Oudina;J. Nanofluids,2022

5. Use of nano fluids in nuclear technology: a review;Iqbal,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Models Based on Dynamics and Discrete Global Optimisation Algorithms;2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS);2024-05-31

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